code: def train_selectors(self, selected_metric=None):
      self.selector_metrics = (balanced_accuracy, roc_auc, log_loss)
      self.selector_files = {}
      self.this_directory = pathlib.Path(__file__).resolve().parent
  
      if selected_metric is not None:
          metric_list = [selected_metric]
      else:
          metric_list = self.selector_metrics
  
      for metric in metric_list:
          training_data_file = (
              self.this_directory
              / metric.name
              / "askl2_training_data.json"
          )
          with open(training_data_file) as fh:
              training_data = json.load(fh)
              fh.seek(0)
              m = hashlib.md5()
              m.update(fh.read().encode("utf8"))
          training_data_hash = m.hexdigest()[:10]
          selector_filename = "askl2_selector_%s_%s_%s_%s.pkl" % (
              autosklearn.__version__,
              sklearn.__version__,
              metric.name,
              training_data_hash,
          )
          selector_directory = os.environ.get("XDG_CACHE_HOME")
          if selector_directory is None:
              selector_directory = pathlib.Path.home()
          selector_directory = (
              pathlib.Path(selector_directory)
              .joinpath("auto-sklearn")
              .expanduser()
          )
          self.selector_files[metric.name] = (
              selector_directory / selector_filename
          )
          metafeatures = pd.DataFrame(training_data["metafeatures"])
          self.strategies = training_data["strategies"]
          y_values = pd.DataFrame(
              training_data["y_values"],
              columns=self.strategies,
              index=metafeatures.index,
          )
          minima_for_methods = training_data["minima_for_methods"]
          maxima_for_methods = training_data["maxima_for_methods"]
          default_strategies = training_data["tie_break_order"]
          if not self.selector_files[metric.name].exists():
              self.required_training = True
              selector = autosklearn.experimental.selector.OVORF(
                  configuration=training_data["configuration"],
                  random_state=np.random.RandomState(1),
                  n_estimators=500,
                  tie_break_order=default_strategies,
              )
              selector = (
                  autosklearn.experimental.selector.FallbackWrapper(
                      selector, default_strategies
                  )
              )
              selector.fit(
                  X=metafeatures,
                  y=y_values,
                  minima=minima_for_methods,
                  maxima=maxima_for_methods,
              )
              self.selector_files[metric.name].parent.mkdir(
                  exist_ok=True, parents=True
              )
  
              try:
                  with open(
                      self.selector_files[metric.name], "wb"
                  ) as fh:
                      pickle.dump(selector, fh)
              except Exception as e:
                  print(
                      "AutoSklearn2Classifier needs to create a selector file under "
                      "the user's home directory or XDG_CACHE_HOME. Nevertheless "
                      "the path {} is not writable.".format(
                          self.selector_files[metric.name]
                      )
                  )
                  raise e
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Mapping, Optional, Union
  import hashlib
  import json
  import os
  import pathlib
  import pickle
  import dask.distributed
  import numpy
  import pandas
  import scipy.sparse
  import sklearn
  from ConfigSpace import Configuration
  import autosklearn
  import autosklearn.experimental.selector
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc
label_desc: Use image node_id 2 for calling the AutoSklearn2Classifier obj's underlying member method code with example usage: obj.train_selectors(selected_metric) without return types